{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/supervised-sentiment-classification-with-cnns","title":"Supervised Sentiment Classification with CNNs for Diverse SE Datasets","arxiv_id":"1812.09653","date":"2018-12-23","proceeding":null,"authors":["Achyudh Ram","Meiyappan Nagappan"],"abstract":"Sentiment analysis, a popular technique for opinion mining, has been used by\nthe software engineering research community for tasks such as assessing app\nreviews, developer emotions in issue trackers and developer opinions on APIs.\nPast research indicates that state-of-the-art sentiment analysis techniques\nhave poor performance on SE data. This is because sentiment analysis tools are\noften designed to work on non-technical documents such as movie reviews. In\nthis study, we attempt to solve the issues with existing sentiment analysis\ntechniques for SE texts by proposing a hierarchical model based on\nconvolutional neural networks (CNN) and long short-term memory (LSTM) trained\non top of pre-trained word vectors. We assessed our model's performance and\nreliability by comparing it with a number of frequently used sentiment analysis\ntools on five gold standard datasets. Our results show that our model pushes\nthe state of the art further on all datasets in terms of accuracy. We also show\nthat it is possible to get better accuracy after labelling a small sample of\nthe dataset and re-training our model rather than using an unsupervised\nclassifier.","url_abs":"http://arxiv.org/abs/1812.09653v1","url_pdf":"http://arxiv.org/pdf/1812.09653v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"supervised-sentiment-classification-with-cnns","repo_url":"https://github.com/achyudhk/SentiGH","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"opinion-mining","task_name":"Opinion Mining"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"},{"task_slug":"sentiment-classification","task_name":"Sentiment Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}